Fl-ML, id-data hija kritika — il-kwalità u l-kwantità tad-data ta' training iddeterminaw fil-biċċa l-kbira il-performance tal-mudell. Il-prinċipju 'garbage in, garbage out' japplika b'mod qawwi: anke algoritmi kbar ifallew ma' data ħażina, filwaqt li data tajba ħafna drabi hija aktar impattanti minn il-għażla tal-algoritmu.
Għaliex id-data hija tant importanti
ML models LEARN from data → the data fundamentally shapes what they learn:
→ GARBAGE IN, GARBAGE OUT → poor data → poor model (no algorithm fixes bad data)
→ good DATA is often MORE impactful than the algorithm (data > model tweaks, often)
→ models can only be as good as the data they learn from
→ data is frequently the most important factor in ML success
